Biology subjects

Lipniacki, T.

Publications and source records attributed to Lipniacki, T..

14 recordsLinked to original sources

bioRxiv2025

A Neyman-Pearson Framework for Modeling Cellular Decision Making Using Single-Cell TNF-NF-κB Signaling Data

Cells make hard calls under noise. When signaling is abnormal, those calls can go wrong and drive pathological conditions and diseases. In this research, we develop a Neyman-Pearson (NP) detection-theory framework that maximizes probability of detection (PD) for a chosen false alarm probability (PFA), without requiring prior probabilities, using experimental single-cell measurements of NF-{kappa}B responses to tumor necrosis factor (TNF), a critical pathway involved in cell survival, apoptosis, immune signaling, and stress response, in wild-type and A20-deficient fibroblasts. We model log-responses as (multi)variate Gaussian and compute optimal thresholds, PD-PFA trade-offs, and ROC curves at 30 minutes and 4 hours. The NP framework captures expected biology: PD increases with TNF dose; wild-type cells outperform A20-/- at matched conditions; and combining two time points (bivariate analysis) improves detection (e.g., for 0.0052 vs. 0.2 ng/mL, PD rises from 0.71 (30 minutes) and 0.42 (4 hours) to 0.80 at PFA = 0.1). The analysis recovers expected biology (higher TNF causes higher detectability; negative feedback lowers late responses) and flags cases where decision quality degrades (e.g., perturbations that blunt separation between conditions). The same recipe extends to multivariate readouts without changing the logic. Overall, the NP detection framework provides a compact, quantitative score of pathway performance and failure. It turns noisy single-cell readouts into actionable decision metrics that compare doses, time points, and perturbations, and ultimately, can help explain when and how cellular decisions drift toward pathology.

systems biology↗

bioRxiv2025

Regulation of the MAPK/ERK system: a computational rule-based model

MAPK/ERK pathway coordinates multiple cellular functions, including proliferation, apoptosis, and motility, yet it is frequently modeled as a one-purpose cascade. Pathway complexity is a consequence of the existence of isoforms of pathway components that are regulated differently and have specific interacting partners. Here, we propose a rule-based model of the MAPK/ERK pathway that accounts for differential regulation of MEK and RAF isoforms and RAF interactions with 14-3-3 proteins. The model addressed signaling based on the enzymatic cascade as well as regulatory protein-protein interactions. We propose that at low concentrations of growth factors, RAS is activated only in a portion of the membrane. This allows the model to reconcile the observed switch-like and graded responses observed at upper and lower tiers of the pathway, respectively. We demonstrate that functional differences between BRAF and CRAF or ARAF can follow from their different interaction with 14-3-3. The 14-3-3 dimers inhibit all RAF isoforms in close form, but preferentially stabilize BRAF-CRAF dimers to BRAF-BRAF homodimers, and may not stabilize RAF dimers without BRAF. The constructed model allows for the exploration of qualitative differences in MAPK/ERK pathway signaling observed in different cell lines.

systems biology↗

bioRxiv2025

Quantifying cancer- and drug-induced changes in Shannon information capacity of RTK signaling

Signaling pathways transmit and process information, enabling cells to respond accurately to external cues. Disease states like cancer can corrupt signal transmission, though the magnitude to which they reduce information capacity has not been quantified. Here we apply pseudo-random pulsatile optogenetic stimulation, live-cell imaging, and information theory to compare the information capacity of receptor tyrosine kinase (RTK) signaling pathways in EML4-ALK-driven lung cancer cells (STE-1) and non-transformed lung epithelial cells (BEAS-2B). The information rate through the RTK/ERK pathway in STE-1 cells was below 0.5 bit/hour but increased to 3 bit/hour after oncogene inhibition. Information was transmitted by only 50-70% of cells, whose channel capacity (maximum information rate) was estimated through in silico protocol optimization. Although oncogene inhibition increased the capacity of the RTK/ERK pathway in STE-1 cells (6 bit/hour), capacity remained lower than in BEAS-2B (11 bit/hour). The capacity of the parallel RTK/calcineurin pathway in BEAS-2B exceeded 15 bit/hour. This study highlights information capacity as a sensitive metric for identifying disease-associated dysfunction and evaluating effects of targeted interventions.

systems biology↗

bioRxiv2024

Type III interferons suppress influenza A virus infection independently of STAT activation by triggering cell death

Type III interferons (IFN-{lambda}1-{lambda}4) are known to limit influenza virus infections in vivo and are non-redundant to type I interferons (IFN- and IFN-{beta}). Here, we demonstrate that IFN-{lambda} acts through mechanisms that are beyond its ability to induce JAK/STAT signaling and promotes fast cell death in epithelial cells stimulated with poly(I:C). Studying influenza A virus (IAV) and respiratory syncytial virus (RSV) infections in vitro we notice that type I interferons, when provided prior to infection, induce higher STAT1/2 activation and a stronger accumulation of proteins coded by interferon-stimulated genes, and correspondingly suppress both IAV and RSV spread more effectively than type III interferons. The knockout of the IFN-{lambda} receptor (subunit IFNLR1), compared to the knockout of the IFN-{beta} receptor (subunit IFNAR1), only slightly influences levels of STAT1/2 phosphorylation during infection with any of the viruses; However, IFNLR1 knockout results in a greater proportion of IAV-infected cells and higher viral RNA and protein levels. We showed that the ratio of dying to infected cells is lower in IFNLR1-deficient cells compared to wild-type cells, suggesting that IFN-{lambda} promotes the rapid death of IAV-infected cells and thereby limits viral spread. This effect was not observed in RSV-infected cultures, possibly due to the RSVs ability to suppress host cell death through its nonstructural proteins. Overall, our results reveal a distinct role for IFN-{lambda} in restricting viral infections by triggering rapid death of infected epithelial cells, in contrast to type I interferons, which are the main inducers of JAK/STAT-mediated expression of ISGs, that may be used by IFN-{lambda} to trigger cell death.

immunology↗

bioRxiv2024

Information transmission in a cell monolayer: A numerical study

Motivated by the spatiotemporal waves of MAPK/ERK activity, crucial for long-range communication in regenerating tissues, we investigated stochastic homoclinic fronts propagating through channels formed by directly interacting cells. We evaluated the efficiency of long-range communication in these channels by examining the rate of information transmission. Our study identified the stochastic phenomena that reduce this rate: front propagation failure, new front spawning, and variability in the front velocity. We found that a trade-off between the frequencies of propagation failures and new front spawning determines the optimal channel width (which geometrically determines the front length). The optimal frequency of initiating new waves is determined by a trade-off between the input information rate (higher with more frequent initiation) and the fidelity of information transmission (lower with more frequent initiation). Our analysis provides insight into the relative timescales of intra- and intercellular processes necessary for successful wave propagation. Author SummaryIn biological tissues, traveling waves of cellular activity are observed in the process of wound healing when they coordinate cell replication and collective migration. These waves can carry information over long distances. However, random effects on the single-cell level can affect wave propagation and disrupt information flow. In this paper, using a numerical model we classified these stochastic events and quantified the maximum range and frequency of such waves and their capacity to carry information. We discovered that most effective transmission occurs in relatively narrow channels (formed by directly interacting cells), and that the refractory time, in which a cell is resistant to activation by neighboring cells, must be long with respect to the time needed for cell activation. The optimal time intervals between the initiated waves are of order of few refractory times (depending on channel length).

systems biology↗

bioRxiv2023

Single Cell Measurements and Modeling and Computation of Decision Making Errors in a Molecular Signaling System with Two Output Molecules

A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, the cell may incorrectly perceive the signals. It may mistakenly behave as if there is a signal - although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system that has two outputs, and introduce and develop methods to model and compute key cell decision making parameters based on the two outputs, and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor {kappa}B (NF{kappa}B) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single cell measured concentration levels of NF{kappa}B and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of a signaling system, one can learn more about complex cellular decision making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions.

systems biology↗

bioRxiv2023

Predictive power of non-identifiable models

Resolving practical nonidentifiability of computational models typically requires either additional data or non-algorithmic model reduction, which frequently results in models containing parameters lacking direct interpretation. Here, instead of reducing models, we explore an alternative, Bayesian approach, and quantify predictive power of non-identifiable models. Considering an example biochemical signalling cascade model as well as its mechanical analog, we demonstrate that by measuring a single variable in response to a properly chosen stimulation protocol, the dimensionality of the parameter space is reduced, which allows for prediction of its trajectory in response to different stimulation protocols even if all model parameters remain unidentified. Successive measurements of remaining variables further constrain model parameters and enable more predictions. We analyse potential pitfalls of the proposed approach that can arise when the investigated model is oversimplified, incorrect, or when the training protocol is inadequate. The main advantage of the suggested iterative approach is that the predictive power of the model can be assessed and practically utilised at each step.

systems biology↗

bioRxiv2022

Antagonism between viral infection and innate immunity at the single-cell level

When infected with a virus, cells may secrete interferons (IFNs) that prompt nearby cells to prepare for upcoming infection. Reciprocally, viral proteins often interfere with IFN synthesis and IFN-induced signaling. We modeled the crosstalk between the propagating virus and the innate immune response using an agent-based stochastic approach. By analyzing immunofluorescence microscopy images we observed that the mutual antagonism between the respiratory syncytial virus (RSV) and infected A549 cells leads to dichotomous responses at the single-cell level and complex spatial patterns of cell signaling states. Our analysis indicates that RSV blocks innate responses at three levels: by inhibition of IRF3 activation, inhibition of IFN synthesis, and inhibition of STAT1/2 activation. In turn, proteins coded by IFN-stimulated (STAT1/2-activated) genes inhibit the synthesis of viral RNA and viral proteins. The striking consequence of these inhibitions is lack of coincidence of viral proteins and IFN expression within single cells. The model enables investigation of the impact of immunostimulatory defective viral particles and signaling network perturbations that could potentially facilitate containment or clearance of the viral infection.

systems biology↗

bioRxiv2022

A plausible identifiable model of the canonicalNF-kB signaling pathway

An overwhelming majority of mathematical models of regulatory pathways, including intensively studied NF-{kappa}B pathway, remains non-identifiable meaning that their parameters may not be determined by existing data. The existing NF-{kappa}B models that are capable to reproduce experimental data, contain non-identifiable parameters, while simplified models with a smaller number of parameters exhibit dynamics that significantly differs from that observed in experiments. Here, we reduce an existing model of the canonical NF-{kappa}B pathway by decreasing the number of equations from 15 to 6 in a way that the resulting model exhibits dynamics closely following that of the original model, both for the nominal and the randomly selected parameters. We carried out the sensitivity-based linear analysis and Monte Carlo-based analysis to demonstrate that the resulting model is structurally and practically identifiable based on a simple TNF stimulation protocol in which 5 model variables are measured. The reduced model is capable to reproduce different types of responses characteristic to regulatory motives controlled by negative feedback loops: nearly-perfect adaptation, damped and sustained oscillations. It can serve as a building block of more comprehensive models of immune responses and cancer, where NF-{kappa}B plays a decisive role. Our approach, although may not be automatically generalized, suggests that other regulatory pathways models can be transformed to identifiable, while retaining their dynamical features.

systems biology↗

bioRxiv2022

The rate of information transmission through the MAPK/ERKsignaling pathway stimulated in a pulsatile manner

Living cells utilize signaling pathways to sense, transduce, and process information. As the extracellular stimulation often has rich temporal characteristics which may govern dynamic cellular responses, it is important to quantify the rate of information flow through the signaling pathways. In this study, we used an epithelial cell line expressing a light-activatable FGF receptor and an ERK activity reporter to assess the ability of the MAPK/ERK pathway to transduce signal encoded in a sequence of pulses. By stimulating the cells with random light pulse trains, we demonstrated that the MAPK/ERK channel capacity is at least 6 bits per hour. The input reconstruction algorithm detects the light pulses with 1-min accuracy 5 min after their occurrence. The high information transmission rate may enable the pathway to coordinate multiple processes including cell movement and respond to rapidly varying stimuli such as chemoattracting gradients created by other cells. Author summaryTo respond to external stimulation, living cells need to transfer information from membrane receptors to downstream proteins that regulate physiological outcomes. In this study we used cells expressing a light-activatable receptor that transmits signals to MAPK/ERK transduction pathway, which regulates numerous physiological processes including proliferation, differentiation and cell motility. By stimulating these cells with various sequences of short light pulses we found that ERK, the last component of the considered signal transduction pathway, may receive at least 6 bits per hour. This bitrate allows 6 binary decisions per hour, enabling rapid responses to varied extracellular stimuli.

systems biology↗

bioRxiv2022

Non-self RNA rewires IFNβ signaling: A mathematical model of the innate immune response

Type I interferons (IFNs) are key coordinators of the innate immune response to viral infection, which through activation of STAT1/2 in bystander cells induce the expression of IFN-stimulated genes (ISGs). The complex system-level mechanisms of IFN signaling are however not well understood. Here, we show that in cells transfected with an analog of viral RNA, poly(I:C), transcriptional activity of STAT1/2 is terminated due to depletion of the interferon {beta} (IFN{beta}) receptor, IFNAR. Two ISGs, RNase L and PKR, not only hinder replenishment of IFNAR, but also suppress negative regulators of IRF3 and NF-{kappa}B, consequently promoting IFN{beta} transcription. We incorporated these findings into a comprehensive mathematical model of innate immunity. By coupling signaling through the IRF3/NF-{kappa}B and STAT1/2 pathways with the activity of RNase L and PKR, the model explains how poly(I:C) switches the transcriptional program from STAT1/2-induced to IRF3/NF-{kappa}B-induced, turning IFN{beta}-responding cells to IFN{beta}-secreting cells. One-sentence summaryA computational model explains how non-self RNA turns cells from IFN{beta}- responders to IFN{beta}-producers.

immunology↗

bioRxiv2021

RSV immunizes bystander cells to IAV infection using interferons β and {lambda}

We observed the interference between two prevalent respiratory viruses, respiratory syncytial virus (RSV) and influenza A virus (IAV, H1N1), and characterized its molecular underpinnings in alveolar epithelial cells (A549). We found that RSV induces higher interferon (IFN) {beta} production than IAV and that IFN{beta} priming confers higher protection against infection with IAV than with RSV. Consequently, we focused on the sequential infection scheme: RSV-then-IAV. Using the A549 WT, IFNAR1 KO, IFNLR1 KO, and IFNAR1-IFNLR1 double KO cell lines we found that both IFN{beta} and IFN{lambda} are necessary for maximum protection against subsequent infection. Immunostaining revealed that preinfection with RSV partitions the cell population into a subpopulation susceptible to subsequent infection with IAV and an IAV-proof subpopulation. Strikingly, the susceptible cells turned out to be those already compromised and efficiently expressing RSV, whereas the bystander, interferon-primed cells are resistant to IAV infection. Thus, the virus-virus exclusion at the cell population level is not realized through a direct competition for a shared ecological niche (single cell) but rather achieved with the involvement of specific cytokines induced within the host innate immune response. ImportanceThe influenza A virus (IAV) and the respiratory syncytial virus (RSV) are common recurrent respiratory infectants, which show a relatively high coincidence. We demonstrated that preinfection with RSV partitions the cell population into a subpopulation susceptible to subsequent infection with IAV and an IAV-proof subpopulation. The susceptible cells are those already compromised and efficiently expressing RSV, whereas the bystander cells are resistant to IAV infection. The cross-protective effect critically depends on IFN{beta} and IFN{lambda} signaling and thus ensues when the proportion of cells preinfected with RSV is relatively low yet sufficient to trigger a pervasive antiviral state in bystander cells. Our study suggests that mild, but not severe, respiratory infections may have a short-lasting protective role against more dangerous respiratory viruses, including SARS-CoV-2.

immunology↗

bioRxiv2019

A Primer on Modeling and Measurement of Signaling Outcomes Affecting Decision Making in Cells: Methods for Determining Optimal and Incorrect Outcomes in Noisy Biochemical Dynamics

Characterization of decision makings in a cell in response to received signals is of high importance for understanding how cell fate is determined. The problem becomes multi-faceted and complex when we consider cellular heterogeneity and dynamics of biochemical processes. In this paper, we present a unified set of decision-theoretic and statistical signal processing methods and metrics to model the precision of signaling decisions, given uncertainty, using single cell data. First, we introduce erroneous decisions that may result from signaling processes, and identify false alarm and miss event that are associated with such decisions. Then, we present an optimal decision strategy which minimizes the total decision error probability. The optimal decision threshold or boundary is determined using the maximum likelihood principle that chooses the hypothesis under which the data are most probable. Additionally, we demonstrate how graphing receiver operating characteristic curve conveniently reveals the trade-off between false alarm and miss probabilities associated with different cell responses. Furthermore, we extend the introduced signaling outcome modeling framework to incorporate the dynamics of biochemical processes and reactions in a cell, using multi-time point measurements and multi-dimensional outcome analysis and decision making algorithms. The introduced multivariate signaling outcome modeling framework can be used to analyze several molecular species measured at the same or different time instants. We also show how the developed binary outcome analysis and decision making approach can be extended to include more than two possible outcomes. To show how the overall set of introduced models and methods can be used in practice and as an example, we apply them to single cell data of an intracellular regulatory molecule called Phosphatase and Tensin homolog (PTEN) in a p53 system, in wild-type and abnormal, e.g., mutant cells. These molecules are involved in tumor suppression, cell cycle regulation and apoptosis. The unified signaling outcome modeling framework presented here can be applied to various organisms ranging from simple ones such as viruses, bacteria, yeast, and lower metazoans, to more complex organisms such as mammalian cells. Ultimately, this signaling outcome modeling approach can be useful for better understanding of transition from physiological to pathological conditions such as inflammation, various cancers and autoimmune diseases. Brief SummaryCells are supposed to make correct decisions, i.e., respond properly to various signals and initiate certain cellular functions, based on the signals they receive from the surrounding environment. Due to signal transduction noise, signaling malfunctions or other factors, cells may respond differently to the same input signals, which may result in incorrect cell decisions. Modeling and quantification of decision making processes and signaling outcomes in cells have emerged as important research areas in recent years. Here we present univariate and multivariate data-driven statistical models and methods for analyzing dynamic decision making processes and signaling outcomes. Furthermore, we exemplify the methods using single cell data generated by a p53 system, in wild-type and abnormal cells.

systems biology↗

bioRxiv2018

Limits to the rate of information transmissionthrough MAPK pathway

Two important signaling pathways of NF-{kappa}B and ERK transmit merely one bit of information about the level of extracellular stimulation. It is thus unclear how such systems can coordinate complex cell responses to external cues. Here, we analyze information transmission in the MAPK/ERK pathway that features relaxation oscillations and responds to EGF by pulses of activated ERK. Based on an experimentally verified computational model of the MAPK/ERK pathway, we demonstrate that when input sequences of EGF pulses are transcoded to output sequences of ERK activity pulses, transmitted information increases nearly linearly with time. Moreover, the information channel capacity C (defined as the upper limit of information that can be transmitted over a sufficiently long time t, divided by t), is not limited by the bandwidth B = 1/{tau}, where {tau} {approx} 1 hour is the relaxation time. Specifically, when input is provided in the form of sequences of short binary EGF pulses separated by varying intervals that are multiples of {tau}/n (but are not shorter than {tau}), then for n = 2, C {approx} 1.39 bit/hour; and for n = 4, C {approx} 1.86 bit/hour. We hypothesize that the primary mode of operation of the MAPK pathway is to translate extracellular growth factor \"bursts\" into precisely timed intracellular ERK \"spikes\" of a predefined amplitude. Such pulse-interval transcoding allows to relay more information than the amplitude-amplitude transcoding considered previously for the ERK and NF-{kappa}B pathways.\n\nAuthor summaryTo coordinate their actions, cells communicate with each other by sending, receiving, and interpreting cytokine signals. Cells recognize chemical identity of signaling molecules and process quantitative and temporal properties of stimulation: amplitude, duration, or frequency. Previous studies indicated that the MAPK pathway transmits about one bit of information about the amplitude (concentration) of a stimulating cytokine, EGF. Sending more information may be enabled by temporal signal modulation. Here, we use the conceptual framework of information theory to support a hypothesis that the MAPK pathway, analyzed as a noisy information channel, reaches maximum information transmission rate when receiving sequences of EGF pulses that are transcoded to sequences of activity pulses of an effector kinase of the pathway, ERK. Since the pathway resetting time is about 1 hour, one could expect that--when sending EGF pulses of \"0\" or \"1\" amplitude every hour--the pathway is able transmit up to 1 bit per hour. We show, however, that when EGF pulses are separated by intervals that are not shorter than 1 hour and are multiples of 15 min (60, 75, 90 min, etc.), information rate can be nearly 2 bits per hour. We hypothesize that high information rate is necessary to control cell proliferation and motility.

systems biology↗